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README.md

@@ -1,199 +1,144 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- laicsiifes/flickr30k-pt-br-human-generated
+language:
+- pt
+metrics:
+- bleu
+- rouge
+- meteor
+- bertscore
+- clipscore
+base_model:
+- microsoft/Phi-3-vision-128k-instruct
+pipeline_tag: image-text-to-text
+model-index:
+- name: Phi-3-Vision-Flickr30K-Native
+  results:
+  - task:
+      name: Image Captioning
+      type: image-text-to-text
+    dataset:
+      name: Flickr30K Portuguese Natively Annotated
+      type: laicsiifes/flickr30k-pt-br-human-generated
+      split: test
+    metrics:
+    - name: CIDEr-D
+      type: cider
+      value: 72.99
+    - name: BLEU@4
+      type: bleu
+      value: 26.74
+    - name: ROUGE-L
+      type: rouge
+      value: 45.78
+    - name: METEOR
+      type: meteor
+      value: 47.45
+    - name: BERTScore
+      type: bertscore
+      value: 72.51
+    - name: CLiP-Score
+      type: clipscore
+      value: 55.10
+license: mit
 ---
+# 🎉 Phi-3 Vision fine-tuned in Flickr30K Translated for Brazilian Portuguese Image Captioning
 
-# Model Card for Model ID
+Phi-3 Vision (microsoft/Phi-3-vision-128k-instruct) model fine-tuned for image captioning on [Flickr30K Portuguese Natively Annotated](https://huggingface.co/datasets/laicsiifes/flickr30k-pt-br-human-generated) (annotated by Brazilian Portuguese speakers).
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+## 🤖 Model Description
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
+## 🧑‍💻 How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- **Install libraries:**
+
+```bash
+pip install transformers==4.45.2 bitsandbytes==0.45.2 peft==0.13.2
+```
+
+- **Python code:**
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
+from huggingface_hub import login
+
+# Use your HuggingFace API key, since Phi-3 Vision is available through user form submission
+login('hf_...')
+
+# load a fine-tuned image captioning model, and corresponding tokenizer and image processor
+model = AutoModelForCausalLM.from_pretrained(
+    'microsoft/Phi-3-vision-128k-instruct',
+    device_map="cuda",
+    trust_remote_code=True,
+    _attn_implementation='eager',
+    quantization_config=BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type='nf4',
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    )
+)
+model.load_adapter('laicsiifes/phi3-vision-flickr30k_pt_human_generated')
+processor = AutoProcessor.from_pretrained('laicsiifes/phi3-vision-flickr30k_pt_human_generated', trust_remote_code=True)
+
+# preprocess an image
+image = Image.open(requests.get("http://images.cocodataset.org/val2014/COCO_val2014_000000458153.jpg", stream=True).raw)
+text_prompt = processor.tokenizer.apply_chat_template(
+    [
+        {
+            "role": "user",
+            "content": f"<|image_1|>\nEscreva uma descrição em português do Brasil para a imagem com no máximo 25 palavras."
+        }
+    ],
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = processor(
+    text=text_prompt,
+    images=image,
+    return_tensors='pt'
+).to('cuda:0')
+
+# generate caption
+generated_ids = model.generate(**inputs, max_new_tokens=25)
+prediction = generated_ids[:, inputs['input_ids'].shape[1]:].tolist()
+generated_text = processor.batch_decode(prediction, skip_special_tokens=True)[0]
+```
+
+```python
+import matplotlib.pyplot as plt
+# plot image with caption
+plt.imshow(image)
+plt.axis("off")
+plt.title(generated_text)
+plt.show()
+```
+
+![image/png](example.png)
+
+## 📈 Results
+
+The evaluation metrics: CIDEr-D, BLEU@4, ROUGE-L, METEOR, BERTScore
+(using [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)), and CLIP-Score (using [CAPIVARA](https://huggingface.co/hiaac-nlp/CAPIVARA)).
+
+| Model | #Params | CIDEr | BLEU-4 | ROUGE-L | METEOR | BERTScore | CLIP-Score |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **ViTucano 1B** | 1.53B | 69.71 | 22.67 | 43.60 | 48.63 | 72.46 | 56.14 |
+| **ViTucano 2B** | 2.88B | 71.49 | 23.75 | 44.30 | **49.49** | **72.60** | 56.47 |
+| **PaliGemma** | 2.92B | 55.30 | 19.41 | 39.85 | 48.96 | 70.33 | **59.96** |
+| **Phi-3 V** | 4.15B | **72.99** | **26.74** | **45.78** | 47.45 | 72.51 | 55.10 |
+| **LLaMa 3.2 V** | 11.70B | 69.13 | 24.79 | 43.11 | 45.99 | 72.08 | 56.38 |
+
+## 📋 BibTeX entry and citation info
+
+Coming soon. For now, please reference the model adapter using its Hugging Face link.